Sentence examples for identifying abbreviation from inspiring English sources

Rule-based methods, such as the Schwartz and Hearst algorithm and Ab3P, are successful at identifying abbreviation definition pairs with high precision.

However, we did not implement or tackle the problem of identifying abbreviations because we believe it is a different problem that can be solved with existing third party solutions such as [16].

Feature weights are then used to identify abbreviation definitions.

We identified abbreviation detection as a supportive task for sentence delineation.

One important enhancement to these corpora encouraged by the BioC format was the specification of the exact location of each identified abbreviation definition.

We used an adapted version of the Hearst and Schwartz algorithm [11] to identify abbreviations of entities found by our system.

Regarding abbreviation resolution, we adapted a simple but effective abbreviation definition recognizer [28], which is based on a set of pattern-matching rules to identify abbreviations and their full forms.

In addition, if a system fails to identify abbreviations, their interpretation by mapping to full forms is impaired.

This difference is due to the fact that the system missed more unique entities than systems using CRFs to identify FAMILY, ABBREVIATION, FORMULA and IDENTIFIER chemical names.

> We tested the three abbreviation identifying modules on the Ab3P, BIOADI, MEDSTRACT and Schwartz and Hearst corpora as shown in Table 3. Results are based on the new gold standard annotations in the four abbreviation corpora.

Functional requirements are identified by the abbreviation "FR" and the business rules by the abbreviation "BR".